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Open Access Research article

A graphical vector autoregressive modelling approach to the analysis of electronic diary data

Beate Wild1*, Michael Eichler2*, Hans-Christoph Friederich1, Mechthild Hartmann1, Stephan Zipfel3 and Wolfgang Herzog1

Author Affiliations

1 Department of General Internal Medicine and Psychosomatics, Medical University Hospital Heidelberg, Heidelberg, Germany

2 Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands

3 Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital Tübingen, Tübingen, Germany

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BMC Medical Research Methodology 2010, 10:28  doi:10.1186/1471-2288-10-28

Published: 1 April 2010

Abstract

Background

In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied.

Methods

We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models.

Results

The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours.

Conclusion

The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.